Tower-crane safety risk is rarely driven by a single defect; it usually emerges from coupled equipment, environmental, operational and management conditions. To make such risk easier to diagnose and control, this study constructed a 45-factor system across nine dimensions: wind load and operating weather, lifting load and operating conditions, surrounding environment, foundation and anchorage, structural connection, mechanisms and key components, electrical control, safety protection and monitoring, and management and emergency capacity. We then developed a two-stage Pearson correlation screening method to reduce redundant indicators without losing dimensional coverage. The first stage combines Pearson correlations with coefficients of variation to remove local redundancy. The second stage uses an information-redundancy contribution index to evaluate the system-level effect of deleting each factor. Based on 178 valid questionnaires, the data showed strong internal consistency and structural suitability (Cronbach’s alpha = 0.961; KMO = 0.898; Bartlett’s p < 0.001). With the final threshold set at p = 0.55, the method retained 16 factors from 45 candidates while preserving all nine dimensions; the retained set remained unchanged when p varied from 0.50 to 0.60. Compared with K-medoids and K-means baselines, the proposed method achieved stronger information explanation and clearer engineering traceability. Fuzzy-set qualitative comparative analysis (fsQCA) further showed that no single condition was necessary for high risk. Six positive truth-table configurations produced three intermediate solution terms (solution consistency = 0.999; solution coverage = 0.490). Across the pathways, adverse weather, lifting demand, surrounding exposure, weak foundation/anchorage and structural-connection weakness repeatedly appeared as core conditions, while mechanism-protection failure and electrical-management failure formed alternative downstream routes. The findings indicate that tower-crane risk develops through coupled pathways rather than isolated factors, and they provide a basis for pathway-oriented inspection and targeted safety control.

Continuous tower crane monitoring enables the recording of changing operating conditions, but interpreting these measurements requires a model of how operational risk states evolve. We combine the Decision-Making Trial and Evaluation Laboratory (DEMATEL), interpretive structural modeling (ISM), and a two-slice dynamic Bayesian network (DBN) for dynamic risk assessment. Six operating indicators define a relative operational risk state from training-referenced percentiles in 60 s time slices. Coverage and activity screening identify eligible observations, while lagged associations and DEMATEL-ISM guide DBN structure screening. The DBN predicts the next-slice state probability and relates each forecast to its inputs through conditional probability contrasts. The case study used one month of records from a tower crane, divided chronologically into training, validation, and test sets. Across 2557 test pairs, the model achieved an area under the receiver operating characteristic curve of 0.6931 and average precision of 0.4571. Its Brier score was 0.1724, compared with 0.1899 for a Markov baseline using only the current risk state. This Brier advantage persisted across eight state definitions, and coupling trend consistently had the largest mean conditional probability contrast. By connecting operating measurements with temporal prediction and factor interpretation, the method supports systematic tracking of changes in tower crane operational risk states.